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Details of Grant 

EPSRC Reference: EP/L005131/1
Title: Explaining Consciousness as Neural Dynamical Complexity
Principal Investigator: Barrett, Dr A
Other Investigators:
Researcher Co-Investigators:
Project Partners:
University of Liege University of Milan
Department: Sch of Engineering and Informatics
Organisation: University of Sussex
Scheme: EPSRC Fellowship
Starts: 01 October 2013 Ends: 30 September 2018 Value (£): 253,830
EPSRC Research Topic Classifications:
Complexity Science
EPSRC Industrial Sector Classifications:
No relevance to Underpinning Sectors
Related Grants:
Panel History:
Panel DatePanel NameOutcome
24 Jul 2013 EPSRC Mathematics Fellowships Interviews - July 2013 Announced
12 Jun 2013 Mathematics Prioritisation Panel Meeting June 2013 Announced
Summary on Grant Application Form
A scientific account of consciousness is a key objective for 21st century science. A large array of consciousness-relevant empirical data has been gathered from rapidly advancing brain imaging technology, and the beginnings of theories accounting for aspects of consciousness have been formed. There is now reason to believe that we are on the verge of a major breakthrough in our understanding of what is special about the particular types of brain activity and brain tasks that are associated with consciousness.

It appears that consciousness involves a precisely balanced amount of communication between the different brain regions. Too much shouting and no region can get on with processing its specifically assigned job. Too little and nothing will be tied together into a unified broadcast across the whole brain. If there is a careful balance between regional segregation and global integration of information, then the globally broadcast content will give rise to a conscious experience.

This project will attempt to measure consciousness by describing this subtle property in mathematical models of brain activity. It aims to predict a person's level of consciousness by analysing brain data alone, e.g. to distinguish between whether they are fully awake, drowsy, or in deep sleep. The project will take advantage of state-of-the-art data, including `intracranial' recordings from surgically implanted electrodes that have, for medical reasons, been placed either on the brain's surface, or deep inside the brain. Analyses will be based on recordings from groups of electrodes that detect electric fields generated by the activity of large populations of neurons. Computer simulations will help identify the signatures of consciousness-related activity in the signals picked up by electrodes. A combination of new mathematical models, measures and statistical techniques will ensure that inferences about consciousness are made reliably based on properties of the data, and not from random fluctuations in activity or by inaccuracies in measurement.

Consciousness science is particularly exciting because what is uncovered has profound implications for our place in nature and for our understanding of our very selves. At the practical level, having a reliable measure of conscious level would be extremely attractive in the clinic. There are scenarios in which traditional assessments of consciousness based on patient behaviour are unreliable. After a serious accident or a stroke, patients can be left in a condition in which they are completely unresponsive, yet could still be conscious and unable to communicate. This research will help inform diagnoses of patients suffering from such a disorder of consciousness, and guide ethical decisions on their treatment. This research will also find applications in psychiatry, since some mental illnesses can be considered as disorders of consciousness. Understanding the complex neural mechanisms of consciousness will open up new avenues for diagnosing mental illness and for treatment of mental suffering.

This project will provide a new way of looking at `complex' systems broadly conceived as any entity that consists of many components that interact in such a way that the whole is greater than the sum of the parts. The mathematical and statistical tools developed will therefore have potential for application far beyond the study of the human brain, for example, to information technology, traffic control, climate change and finance, to name just a few domains in which complex systems arise.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.sussex.ac.uk